Connect Buildkite and quicktable to Build Intelligent Automations
Enable Integrations or automations with these events of Buildkite and quicktable
Enable Integrations or automations with these events of Buildkite and quicktable
Actions
Create YAML Pipeline
This action creates a YAML pipeline.
Create Cluster
This action creates a new cluster for organizing build agents.
Update YAML Pipeline
This action updates exisiting YAML Pipeline.
Delete Pipeline By Slug Name
Delete Exisiting Pipeline By Name.
Search Cluster By Name
This action search cluster by name.
Update Cluster
This action updates the cluster.
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Frequently Asked Questions
To start, connect both your Buildkite and quicktable accounts to viaSocket. Once connected, you can set up a workflow where an event in Buildkite triggers actions in quicktable (or vice versa).
Absolutely. You can customize how Buildkite data is recorded in quicktable. This includes choosing which data fields go into which fields of quicktable, setting up custom formats, and filtering out unwanted information.
The data sync between Buildkite and quicktable typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between Buildkite and quicktable. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About Buildkite
Buildkite is a platform that provides powerful and scalable continuous integration and continuous delivery (CI/CD) pipelines. It allows developers to automate their software development processes, from testing to deployment, ensuring high-quality code and faster release cycles.
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About quicktable
QuickTable is a visual data modeling tool that lets everyone clean and transforms data without writing code. Using QuickTable helps reduce the time it takes to prepare data for analytics and modeling by up to 70 percent compared to traditional ways.
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